Follow the data

Design and Build of Large-Scale Data Platforms

The Foundation of Data Science and AI

As the volume of data being generated by organisations growings exponentially, it is increasingly difficult to store and manage the data and it can be challenging to distill the data down into actionable business insight. Data platforms have to be carefully engineered to ensure that they scale and grow with the organisation, and act as an enabler for data-lead innovations such as machine learning and artificial intelligence.

We work with our clients on data engineering projects to build robust data platforms for ingesting, transforming and storing large scale datasets. We also work to catalogue the data and document the schemas, and to make the data available to business users via the appropriate business intelligence and data science tools.

We currently specialise in building data platforms on Databricks and Google BigQuery, but have experience of many other platforms including a range of Microsoft Azure data analytics products.

Data engineering is the foundation behind data science, machine learning, AI, and business intelligence. To prevent your organisation from drowning in unusable data, and to allow your teams to make informed business decisions based on data insights, a structured data engineering approach is required. This includes the compilation of structured data sets, ongoing verification of data quality and integrity, data protection and security, and efficiently distributing the data to those who need it.

Dan Norris-Jones, CTO, Priocept


Whether your organisation is yet to start its journey towards building a data platform or is already well on the way, we can you help develop a strategy and architecture that will deliver a cost effective, highly automated, secure, and robust platform.

A typical data engineering project will start with the process of identifying and cataloguing datasets within an organisational domain, whether pre-existing or required in the future, mapping out the sources and consumers of the data, and defining the ownership, governance and integrity constraints of the data.


The number of “big data” products in the market is almost limitless and can be difficult to navigate. Priocept can help you to select the most appropriate technology stack for the data volumes, data structures, available budget and technical skills within your organisation.

When recommending suitable data products we will also consider the integration of your data platform into your overall technology landscape, including the cloud infrastructure platforms already in use and pre-existing business reporting and office productivity tools.


We work with our clients to design, build and operate their data platforms.

We design data platforms in line with “Data Mesh” concepts, including data as a product, self-serve access to data infrastructure, and federated data governance.

We then apply modern software engineering best practices to the field of data engineering such as infrastructure-as-code, continuous delivery and test automation, to deliver a robust, flexible and easily operated data platform.